It’s a marketer’s dream to be able to predict a customer’s next move and make decisions based off that. But with an increasing volume of customer data at their fingertips, marketers need technology to help with the almost overwhelming task. eMarketer’s Tricia Carr spoke with Radoslaw Dobrolecki, US business development director at retargeting technology company RTB House, about how artificial intelligence (AI) can help predict customer behavior at scale—even for the segment of one—and the challenges marketers face in this arena.

eMarketer: Many marketers are implementing predictive technology to anticipate what their customers will do next. Where does artificial intelligence come in here?

Radoslaw Dobrolecki: It’s crucial to be very good at evaluating the value of a user, and to do that well, you need to predict the behavior of that user. This is all about deploying AI. Consumers exhibit so many behaviors online and there are so many equations you need to look at in order to predict their behavior and decide, at the end of the day, if they’re going to partake in your brand.

For example, you want to look at their preferences when they’re browsing online. What product categories are they looking at, and how long do they spend looking at those products? What products are they adding to their cart, and what do they end up buying? But you also want to look at the broader picture. What have they done in the past? How long was their last visit to your site? And on top of that, you want to look at what products and creative you’ve shown them.

This is a lot of data. To be able to predict their behavior, you need to be able to analyze all of this data very efficiently, and that’s where AI technology comes in.

eMarketer: What new doors has AI opened up for making predictions about customers’ future behaviors or preferences?

Dobrolecki: Computer vision is a very interesting aspect of AI. While AI technology allows us to be more efficient at analyzing visual data, computer vision—which we’ve already implemented with our technology—allows us to look at product images that you want to display to a user, analyze products based on their similarities and differences, and advertise new products that are similar to users’ past preferences. This goes hand in hand with predicting user behavior.

If a fashion brand is launching a new collection, historically they would need to collect data on the performance of those new products to be able to make well-educated decisions. Should I show this skirt, these pants or these shoes to users? With computer vision, you can analyze new products, compare them to the performance of previous similar products and efficiently predict which of those products will be popular among a certain group of users.

eMarketer: With the help of AI, are marketers getting close to making predictions about a single customer rather than a customer segment, at scale?

Dobrolecki: Marketers used to segment their customers into broader audiences to make decisions about what they should advertise to them, but that needs to change now. They need to be way more granular in their approach, and AI is helping us do that.

With deep learning AI algorithms, you’re able to look at and analyze one particular user and draw conclusions quickly so you can advertise to them on a personal level. Putting a customer in segment X won’t be enough in the future—we have to have a segment-of-one approach.

eMarketer: I see many irrelevant ads on a daily basis, so it does seem like marketers can do better.

Dobrolecki: It’s actually a paradox—on one hand, consumers want to see more relevant ads, but on the other, they want to limit the amount of data that external parties can collect about them. In 2018 and further into the future, this will be a big challenge for everyone in the marketplace. Look at what Apple did with iOS 11—they’re limiting the use of third-party cookies, which prevents the tracking of users. The whole industry will have to face this challenge soon. If cookies aren’t enough in the future, what will they do next?

eMarketer: If this continues to be the case, how will it play out next year? Have marketers figured out how to strike a balance?

Dobrolecki: It’s still a big challenge, but AI will help them be more accurate as they decide what ad impressions to buy. Marketers will end up buying less impressions, because the impressions will cost more but have a bigger payoff. It’s a win-win-win—it’s a win for consumers because they won’t be bombarded with banner ads all the time and they’ll see more relevant content, it’s a win for marketers because they can be more precise and it’s a win for publishers because advertisers will pay higher CPMs.

And we also have to remember that there’s a whole advertising ecosystem. Of course consumers want to protect their data, but at the same time, they consume content online. This content is created by publishers that live off advertising, and most consumers share their data with them. It’s a closed circle.

It doesn’t look like Google will do as drastic a move as Apple in 2018 because they own a big part of the advertising ecosystem. The rest of the marketplace won’t follow Apple completely.